CN109816225B - Task scheduling method based on forklift cloud platform - Google Patents
Task scheduling method based on forklift cloud platform Download PDFInfo
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- CN109816225B CN109816225B CN201910028413.3A CN201910028413A CN109816225B CN 109816225 B CN109816225 B CN 109816225B CN 201910028413 A CN201910028413 A CN 201910028413A CN 109816225 B CN109816225 B CN 109816225B
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Abstract
The invention discloses a task scheduling method based on a forklift cloud platform, which relies on the forklift cloud platform to perform data modeling on a scene according to data uploaded to the forklift cloud platform and objectively estimate daily average transport total amount in the scene compared with a traditional first-come first-serve task scheduling mode. After the scene model is built, the carrying priority of the vehicle is set according to the forklift configuration condition in the scene and by referring to the tonnage and the transportation convenience of the vehicle, and the fleet is subjected to task scheduling through a priority algorithm to realize the rapid carrying of the goods. The problem of traditional first come first serve task scheduling mode, at the final stage of transport, the condition that the reciprocating no-load of light-tonnage fork truck is many times and is returned is solved for last transport once ends, avoids the condition that motorcade transport capacity is not enough or transport capacity is surplus, improves the transport efficiency of motorcade.
Description
Technical Field
The invention relates to the field of car networking control, in particular to a task scheduling method based on a forklift cloud platform.
Background
At the beginning of design, the load tonnage of the vehicle is marked on a nameplate of the conventional electric forklift, and workers are informed of the maximum lifting weight of the vehicle. In the actual use process, because the total weight of the goods transported in the scene is not known in advance, the goods are transported by adopting the principle that the fork truck firstly comes and firstly serves, so that part of the vehicles in the fleet are in high-load work, and part of the vehicles are in idle states.
At present, in the implementation of task scheduling of a forklift, the mode adopted is a first-come first-serve mode, also called a first-in first-out mode, which means that no matter which type or tonnage forklift comes to carry goods, the forklift starts carrying first before arriving, the task scheduling method is the most conventional method at present, but the average completion period of a carrying task depends on the time for completing carrying of each vehicle, and the carrying task is carried to the last stage, if the carrying capacity of the subsequent forklift is insufficient, the forklift with sufficient carrying capacity needs to wait until the forklift with sufficient carrying capacity can smoothly complete the carrying task, so that the time is greatly wasted, and the working efficiency of a fleet is influenced.
In the conventional task scheduling scheme, as shown in fig. 1, the operation is performed in accordance with the carrying sequence of the fork truck a, B, C, …. At the final stage of carrying, when the weight of the residual goods of the forklift A on a backward road is greater than the carrying capacity of the forklift B, the forklift C and the follow-up forklift, the forklift B, the forklift C and the follow-up forklift always run one time without load and return, the completion of the whole carrying needs to wait for the completion of the forklift A, so that the resource allocation is uneven, and the working efficiency of a motorcade is reduced.
Therefore, it is a problem worth studying to provide a dispatching method capable of improving the comprehensive working efficiency of the forklift.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a task scheduling method based on a forklift cloud platform.
The purpose of the invention is realized as follows:
a task scheduling method based on a forklift cloud platform comprises the following steps:
step 1): logging in a forklift cloud platform, if task scheduling is carried out for the first time by using the forklift cloud platform, firstly inputting basic information and load information of an existing vehicle and an estimated value of total carrying weight in a scene, and then clicking a task scheduling button on the platform, sending suggested fleet configuration information to a bound mobile phone number; if the forklift cloud platform is not used for the first time, and the task scheduling button is directly clicked, suggested fleet configuration information is sent to the bound mobile phone number;
step 2): after the task scheduling button is clicked, the platform performs data analysis through scene data stored in the database, and mainly models the carrying weight in the scene. When the operation of the forklift in the scene is carried out at every time, the carried weight is uploaded to the forklift cloud platform, the forklift cloud platform carries out data cleaning on all the uploaded weights, the effective data values are reserved and then accumulated, and the accumulated data are stored in the database. And starting a task scheduling function every time, and predicting the carrying weight through a linear regression algorithm based on the data in the database to obtain an estimated value of the goods required to be carried at this time. And then selecting the vehicles in the scene according to a vehicle priority configuration algorithm, and selecting the forklifts A, B, C and the like which accord with task scheduling according to the tonnage of the vehicles and the running average speed as selection basis to perform task scheduling.
Step 3): sorting the priority carrying sequence of the vehicles according to the tonnage of the vehicles and the running speed of the vehicles, and starting carrying operation of cargos;
step 4): after each transportation of the vehicle is completed, the weight value of the actual transportation is transmitted to a forklift cloud platform through a GPRS module through a network protocol for the next calculation;
step 5): after the forklift cloud platform receives the uploaded weight value every time, subtracting the uploaded weight value from the original estimated value to obtain the remaining carrying weight, reconfiguring the fleet of the picked vehicles again according to a vehicle priority configuration algorithm, rearranging a carrying sequence, and maximally utilizing resources;
step 6): and when the carrying weight reaches the maximum tonnage in the configured fleet, the forklift cloud platform informs the forklift with the maximum tonnage to carry out carrying work again, and meanwhile, other forklifts are not allocated with work, so that the task scheduling work of carrying can be finished only once at last, and the maximum utilization of forklift resources is realized.
Has the positive and beneficial effects that: the invention relies on the forklift cloud platform, and the forklift cloud platform monitors core indexes such as the running state, the working time, the carrying weight and the like of the forklift in real time, so that the motorcade configuration, the daily cargo carrying capacity and the motorcade operation time in the forklift working scene can be effectively analyzed, data modeling is carried out on the scene according to the data uploaded to the forklift cloud platform, and the daily average carrying total amount in the scene is objectively estimated. After the scene model is built, the carrying priority of the vehicle is set according to the forklift configuration condition in the scene and by referring to the tonnage and the transportation convenience of the vehicle, and the fleet is subjected to task scheduling through a priority algorithm, so that the rapid carrying of the goods is realized. Because the total weight of the actually required transported goods is estimated before the goods are transported, when the forklift is dispatched and distributed by tasks, the situation that the transport capacity of the forklift is insufficient or excessive can be avoided, the waste of resources is avoided, and the transport efficiency of the forklift is improved.
Drawings
FIG. 1 is a prior art scheduling flow diagram;
FIG. 2 is a flow chart of the scheduling of the present invention;
FIG. 3 is a cloud platform architecture diagram of the present invention;
FIG. 4 is a diagram of a scheduling architecture of the present invention.
Detailed Description
The invention will be further explained with reference to the accompanying drawings:
as shown in fig. 2 to 4, a task scheduling method based on a forklift cloud platform includes the following steps:
step 1): logging in a forklift cloud platform, if task scheduling is carried out for the first time by using the forklift cloud platform, firstly inputting basic information and load information of an existing vehicle and an estimated value of total carrying weight in a scene, and then clicking a task scheduling button on the platform, sending suggested fleet configuration information to a bound mobile phone number; if the forklift cloud platform is not used for the first time, and the task scheduling button is directly clicked, suggested fleet configuration information is sent to the bound mobile phone number;
step 2): after the task scheduling button is clicked, the platform performs data analysis through scene data stored in the database, and mainly models the carrying weight in the scene. When the operation of the forklift in the scene is carried out at every time, the carried weight is uploaded to the forklift cloud platform, the forklift cloud platform carries out data cleaning on all the uploaded weights, the effective data values are reserved and then accumulated, and the accumulated data are stored in the database. And starting a task scheduling function every time, and predicting the carrying weight through a linear regression algorithm based on the data in the database to obtain an estimated value of the goods required to be carried at this time. Selecting vehicles in the scene according to a vehicle priority configuration algorithm, and selecting a forklift A, a forklift B, a forklift C and the like which accord with task scheduling according to the tonnage of the vehicles and the running average speed as selection basis;
step 3): sorting the priority carrying sequence of the vehicles according to the tonnage of the vehicles and the running speed of the vehicles, and starting carrying operation of cargos;
step 4): after each transportation of the vehicle is completed, the weight value of the actual transportation is transmitted to a forklift cloud platform through a GPRS module through a network protocol for the next calculation;
step 5): after the forklift cloud platform receives the uploaded weight value every time, the original estimated value is used for subtracting the uploaded weight value to obtain the remaining carrying weight, the vehicle fleet is reconfigured again in the sorted vehicles according to the vehicle priority configuration algorithm, the carrying sequence is rearranged, and resources are utilized to the maximum.
Step 6): and when the carrying weight reaches the maximum tonnage in the configured fleet, the forklift cloud platform informs the forklift with the maximum tonnage to carry out carrying work again, and meanwhile, other forklifts are not allocated with work, so that the task scheduling work of carrying can be finished only once at last, and the maximum utilization of forklift resources is realized.
A data acquisition unit: the method comprises the steps of collecting and processing data uploaded by the forklift in real time, filtering in the first step, eliminating obvious error data, guaranteeing the reliability of the uploaded data, analyzing and classifying the collected data regularly, such as carrying weight, working mileage, driving speed, working time and the like, and providing a data source for the next data analysis.
A data analysis unit: and analyzing and concluding the acquired data to obtain a general operation rule. The analysis result closest to the real situation is obtained through analysis and operation of huge data quantity, and the analysis result is continuously compared and regressed with the actual result, so that theoretical support is provided for the next modeling operation.
A modeling operation unit: by carrying out deep-level inductive summarization on the analysis result, the on-site working condition, the motorcade condition and the driving habit are modeled on the platform, and the on-site real situation is reflected more truly.
An execution unit: and on the basis of modeling, scheduling, distributing and remotely monitoring the tasks of the fleet.
Compared with the traditional first-come first-serve task scheduling mode, the method relies on the forklift cloud platform, carries out data modeling on the scene according to the data uploaded to the forklift cloud platform, and objectively estimates the daily average carrying total amount in the scene. After the scene model is built, the carrying priority of the vehicle is set according to the forklift configuration condition in the scene and by referring to the tonnage and the transportation convenience of the vehicle, and the fleet is subjected to task scheduling through a priority algorithm to realize the rapid carrying of the goods. The problem of traditional first come first serve task scheduling mode, at the final stage of transport, the condition that the reciprocating no-load of light-tonnage fork truck is many times and is returned is solved for last transport once ends, avoids the condition that motorcade transport capacity is not enough or transport capacity is surplus, improves the transport efficiency of motorcade.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (1)
1. A task scheduling method based on a forklift cloud platform is characterized by comprising the following steps:
step 1): logging in a forklift cloud platform, if task scheduling is carried out for the first time by using the forklift cloud platform, firstly inputting basic information and load information of an existing vehicle and an estimated value of total carrying weight in a scene, and then clicking a task scheduling button on the platform, sending suggested fleet configuration information to a bound mobile phone number; if the forklift cloud platform is not used for the first time, and the task scheduling button is directly clicked, suggested fleet configuration information is sent to the bound mobile phone number;
step 2): after a task scheduling button is clicked, the platform carries out data analysis through scene data stored in a database, mainly carries out modeling on the carrying weight in a scene, and estimates the daily average carrying total amount in the scene; according to the configuration condition of the forklifts in the scene, the carrying priority of the vehicles is set by referring to the tonnage and the transportation convenience of the vehicles, the vehicles in the scene are selected according to a vehicle priority configuration algorithm, and the forklifts A, B and C which accord with task scheduling are selected for task scheduling according to the tonnage and the running average speed of the vehicles;
step 3): sorting the priority carrying sequence of the vehicles according to the tonnage of the vehicles and the running speed of the vehicles, and starting carrying operation of cargos; reciprocating from the goods carrying point to the unloading point;
step 4): after each transportation of the vehicle is completed, the weight value of the actual transportation is transmitted to a forklift cloud platform through a GPRS module through a network protocol for the next calculation;
step 5): after the forklift cloud platform receives the uploaded weight value every time, subtracting the uploaded weight value from the original estimated value to obtain the remaining carrying weight, reconfiguring the fleet of the picked vehicles again according to a vehicle priority configuration algorithm, rearranging a carrying sequence, and maximally utilizing resources;
step 6): at the end moment of the carrying operation, namely the last moment of task scheduling, the forklift cloud platform continuously predicts the residual carrying weight; when the carrying weight reaches the maximum tonnage in the configured fleet, the forklift cloud platform informs the forklift with the maximum tonnage of carrying again, and meanwhile, other forklifts are not allocated with work, so that the task scheduling work of carrying can be finished only once at last, and the maximum utilization of forklift resources is realized;
in the step 2), when the forklift in the scene performs operation each time, the carried weight is uploaded to a forklift cloud platform, the forklift cloud platform performs data cleaning on all the uploaded weights, effective data values are reserved and then accumulated, and the accumulated data are stored in a database; starting a task scheduling function every time, and predicting the carrying weight through a linear regression algorithm based on data in a database to obtain an estimated value of the goods needing to be carried at this time;
the method also comprises the steps of collecting and processing the data uploaded by the forklift in real time, filtering in the first step, eliminating obvious error data, ensuring the reliability of the uploaded data, and analyzing and classifying the collected data regularly.
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| CN109086994A (en) * | 2018-07-31 | 2018-12-25 | 河北工业大学 | It is produced towards quantity-produced and transports combined scheduling method |
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| US8386177B2 (en) * | 2009-05-13 | 2013-02-26 | Taiwan Mobile Communication | Vehicle-dispatching method and vehicle-dispatching system |
| CN104240054A (en) * | 2014-08-13 | 2014-12-24 | 福州大学 | Implementation method of logistics vehicle dispatching based on particle swarms |
| CN108009023B (en) * | 2017-11-29 | 2022-06-03 | 武汉理工大学 | Task scheduling method based on BP neural network time prediction in hybrid cloud |
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| CN107093160A (en) * | 2017-04-17 | 2017-08-25 | 无锡北斗星通信息科技有限公司 | Airport adaptive management system |
| CN109086994A (en) * | 2018-07-31 | 2018-12-25 | 河北工业大学 | It is produced towards quantity-produced and transports combined scheduling method |
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